# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Heart_rate"
cohort = "GSE35661"

# Input paths
in_trait_dir = "../DATA/GEO/Heart_rate"
in_cohort_dir = "../DATA/GEO/Heart_rate/GSE35661"

# Output paths
out_data_file = "./output/preprocess/3/Heart_rate/GSE35661.csv"
out_gene_data_file = "./output/preprocess/3/Heart_rate/gene_data/GSE35661.csv"
out_clinical_data_file = "./output/preprocess/3/Heart_rate/clinical_data/GSE35661.csv"
json_path = "./output/preprocess/3/Heart_rate/cohort_info.json"

# Get file paths
soft_path, matrix_path = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_path)

# Get unique values by row in clinical data and limit the number shown
sample_chars = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in sample_chars.items():
    print(f"\n{feature}:")
    print(values)
# 1. Gene Expression Data Availability
# Series title suggests transcriptional data and U133+2 arrays, so it's likely gene expression data
is_gene_available = True

# 2. Variable Availability and Type Conversion
# Heart rate data is available in row 2
trait_row = 2

# Age data not available
age_row = None 

# Gender data available in row 0
gender_row = 0

def convert_trait(val):
    if pd.isna(val):
        return None
    try:
        # Extract numeric value after "heart rate (bpm):"
        val = val.split(":")[-1].strip()
        return float(val)
    except:
        return None

def convert_age(val):
    # Age not available
    return None

def convert_gender(val):
    if pd.isna(val):
        return None
    try:
        # Extract value after colon
        val = val.split(":")[-1].strip().lower()
        if val == "male":
            return 1
        elif val == "female":
            return 0
        return None
    except:
        return None

# 3. Save Metadata 
is_trait_available = trait_row is not None
validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=is_trait_available)

# 4. Clinical Feature Extraction
if trait_row is not None:
    clinical_features = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    # Preview the extracted features
    preview = preview_df(clinical_features)
    
    # Save clinical features
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data
gene_data = get_genetic_data(matrix_path)

# Print first 20 probe/gene IDs
print("First 20 probe/gene IDs:")
print(gene_data.index[:20].tolist())
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_annotation = get_gene_annotation(soft_path)

# Preview column names and first few values
column_preview = preview_df(gene_annotation)
print("\nGene annotation columns and sample values:")
print(column_preview)
# Since we have Ensembl transcript IDs (ENST), we should use direct gene symbol
# normalization rather than probe-to-gene mapping
# First normalize the transcript IDs by removing '_at' suffix
gene_data.index = gene_data.index.str.replace('_at$', '', regex=True)

# Normalize gene symbols using NCBI gene synonym dictionary
gene_data = normalize_gene_symbols_in_index(gene_data)

# Preview result
print("\nFirst 20 normalized gene symbols:")
print(gene_data.index[:20].tolist())
# Get mapping from annotation data
mapping_df = gene_annotation[['ID', 'Gene Symbol']].copy()
mapping_df = mapping_df.rename(columns={'Gene Symbol': 'Gene'})
# Remove trailing '_at' from IDs to match gene_data
mapping_df['ID'] = mapping_df['ID'].str.replace('_at$', '', regex=True)

# Convert probe measurements to gene expression values 
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Normalize gene symbols and save gene data
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

# Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# Check for biases and remove biased demographic features
trait_type = 'binary' if len(linked_data[trait].unique()) == 2 else 'continuous'
if trait_type == "binary":
    is_biased = judge_binary_variable_biased(linked_data, trait)
else:
    is_biased = judge_continuous_variable_biased(linked_data, trait)

# Remove biased demographic features
if "Age" in linked_data.columns:
    if judge_continuous_variable_biased(linked_data, "Age"):
        linked_data = linked_data.drop(columns="Age")
if "Gender" in linked_data.columns:
    if judge_binary_variable_biased(linked_data, "Gender"):
        linked_data = linked_data.drop(columns="Gender")

# Validate and save cohort info
note = "The dataset contains before/after exercise measurements for each subject. We merged them to increase statistical power."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort, 
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available,
    is_biased=is_biased,
    df=linked_data,
    note=note
)

# Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)